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Keywords = RANS closures

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23 pages, 9064 KiB  
Article
A Computational Thermo-Fluid Dynamics Simulation of Slot Jet Impingement Using a Generalized Two-Equation Turbulence Model
by Antonio Mezzacapo, Rossella D’Addio and Giuliano De Stefano
Energies 2025, 18(14), 3862; https://doi.org/10.3390/en18143862 - 20 Jul 2025
Viewed by 1007
Abstract
In this study, a computational thermo-fluid dynamics simulation of a wide-slot jet impingement heating process is performed. The present configuration consists of a turbulent incompressible air jet impinging orthogonally on an isothermal cold plate at a Reynolds number of around 11,000. The two-dimensional [...] Read more.
In this study, a computational thermo-fluid dynamics simulation of a wide-slot jet impingement heating process is performed. The present configuration consists of a turbulent incompressible air jet impinging orthogonally on an isothermal cold plate at a Reynolds number of around 11,000. The two-dimensional mean turbulent flow field is numerically predicted by solving Reynolds-averaged Navier–Stokes (RANS) equations, where the two-equation eddy viscosity k-ω model is utilized for turbulence closure. As the commonly used shear stress transport variant overpredicts heat transfer at the plate due to excessive turbulent diffusion, the recently developed generalized k-ω (GEKO) model is considered for the present analysis, where the primary model coefficients are suitably tuned. Through a comparative analysis of the various solutions against one another, in addition to reference experimental and numerical data, the effectiveness of the generalized procedure in predicting both the jet flow characteristics and the heat transfer at the plate is thoroughly evaluated, while determining the optimal set of model parameters. By improving accuracy within the RANS framework, the importance of model adaptability and parameter tuning for this specific fluid engineering application is demonstrated. This study offers valuable insights for improving predictive capability in turbulent jet simulations with broad engineering implications, particularly for industrial heating or cooling systems relying on wide-slot jet impingement. Full article
(This article belongs to the Special Issue Computational Fluids Dynamics in Energy Conversion and Heat Transfer)
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22 pages, 4427 KiB  
Article
Numerical Investigation of Cavitation Models Combined with RANS and PANS Turbulence Models for Cavitating Flow Around a Hemispherical Head-Form Body
by Hyeri Lee, Changhun Lee, Myoung-Soo Kim and Woochan Seok
J. Mar. Sci. Eng. 2025, 13(4), 821; https://doi.org/10.3390/jmse13040821 - 21 Apr 2025
Viewed by 703
Abstract
Accurate prediction of cavitating flows is essential for improving the performance and durability of marine and hydrodynamic systems. This study investigates the influence of different cavitation models—Kunz, Merkle, and Schnerr–Sauer—on the numerical prediction of cavitation around a hemispherical head-form body using computational fluid [...] Read more.
Accurate prediction of cavitating flows is essential for improving the performance and durability of marine and hydrodynamic systems. This study investigates the influence of different cavitation models—Kunz, Merkle, and Schnerr–Sauer—on the numerical prediction of cavitation around a hemispherical head-form body using computational fluid dynamics (CFD). Additionally, the effects of turbulence modeling approaches, including Reynolds-averaged Navier–Stokes (RANS) and partially averaged Navier–Stokes (PANS), are examined to assess their capability in capturing transient cavitation structures and turbulence interactions. The results indicate that the Schnerr–Sauer model, which incorporates bubble dynamics based on the Rayleigh–Plesset equation, provides the most accurate prediction of cavitation structures, closely aligning with experimental data. The Merkle model shows intermediate accuracy, while the Kunz model tends to overpredict cavity closure, limiting its ability to capture unsteady cavitation dynamics. Furthermore, the PANS turbulence model demonstrates superior performance over RANS by resolving more transient cavitation phenomena, such as cavity shedding and re-entrant jets, leading to improved accuracy in pressure distribution and vapor volume fraction predictions. The combination of the PANS turbulence model with the Schnerr–Sauer cavitation model yields the most consistent results with experimental observations, highlighting its effectiveness in modeling highly dynamic cavitating flows. Full article
(This article belongs to the Section Ocean Engineering)
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23 pages, 43085 KiB  
Article
Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
by Guolin Ma, Linlin Tian, Yilei Song and Ning Zhao
Appl. Sci. 2024, 14(23), 11438; https://doi.org/10.3390/app142311438 - 9 Dec 2024
Cited by 1 | Viewed by 1313
Abstract
The correct prediction of the wind speed and turbulence levels over complex terrain is essential for accurately assessing wind turbine wake recovery, power production, safety, and wind farm design. In this paper, two modified RANS turbulence models are proposed, which are innovative variants [...] Read more.
The correct prediction of the wind speed and turbulence levels over complex terrain is essential for accurately assessing wind turbine wake recovery, power production, safety, and wind farm design. In this paper, two modified RANS turbulence models are proposed, which are innovative variants of the conventional SST k-ω model and the linear Reynolds stress model (RSM) featuring optimized closure constants. Then, these two modified models and their origin models are applied to compare and analyze wind flows from a 3D hill wind tunnel experiment and two field measurements over typical complex terrain, including Askervein hill and Bolund island, with the aim of analyzing the sensitivity of wind flows to different RANS turbulence models. The study focuses on analyzing the effects of different turbulence models on the self-sustainability of wind speed and turbulent kinetic energy upstream of the computational domain and on the accuracy of wind flow prediction over complex terrain. The results show that our modified RSM model shows better agreement with the available experimental data on the upstream and leeward sides of all simulated hills. The wind speed on the leeward slope is particularly sensitive to the turbulence model, with a maximum difference in the relative root mean square error (RRMSE) that can reach 11% among the four models. The accuracy of the turbulent kinetic energy depends on the self-sustainability of the upstream turbulent kinetic energy and the predictive ability of the turbulence model for separated flows, and the maximum difference in the RRMSE of the four models can reach 47%. In addition, the advantages and disadvantages of the tested models are discussed to provide guidance for model selection during wind flow simulations in complex terrain. Full article
(This article belongs to the Special Issue Recent Advances in Wind Engineering and Applied Aerodynamics)
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22 pages, 6213 KiB  
Article
Simulation of the Neutral Atmospheric Flow Using Multiscale Modeling: Comparative Studies for SimpleFoam and Fluent Solver
by Zihan Zhao, Lingxiao Tang and Yiqing Xiao
Atmosphere 2024, 15(10), 1259; https://doi.org/10.3390/atmos15101259 - 21 Oct 2024
Cited by 1 | Viewed by 1024
Abstract
The reproduced planetary boundary layer (PBL) wind is commonly applied in downscaled simulations using commercial CFD codes with Reynolds-averaged Navier–Stokes (RANS) turbulence modeling. When using the turbulent inlets calculated by numerical weather prediction models (NWP), adjustments of the turbulence eddy viscosity closures and [...] Read more.
The reproduced planetary boundary layer (PBL) wind is commonly applied in downscaled simulations using commercial CFD codes with Reynolds-averaged Navier–Stokes (RANS) turbulence modeling. When using the turbulent inlets calculated by numerical weather prediction models (NWP), adjustments of the turbulence eddy viscosity closures and wall function formulations are concerned with maintaining the fully developed wind profiles specified at the inlet of CFD domains. The impact of these related configurations is worth discussing in engineering applications, especially when commercial codes restrict the internal modifications. This study evaluates the numerical performances of open-source OpenFOAM 2.3.0 and commercial Fluent 17.2 codes as supplementary scientific comparisons. This contribution focuses on the modified turbulence closures to incorporate turbulent profiles produced from mesoscale PBL parameterizations and the modified wall treatments relating to the roughness length. The near-ground flow features are evaluated by selecting the flat terrains and the classical Askervein benchmark case. The improvement in near-ground wind flow under the downscaled framework shows satisfactory performance in the open-source CFD platform. This contributes to engineers realizing the micro-siting of wind turbines and quantifying terrain-induced speed-up phenomena under the scope of wind-resistant design. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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18 pages, 2451 KiB  
Article
Strategies for Enhancing One-Equation Turbulence Model Predictions Using Gene-Expression Programming
by Tony Di Fabbio, Yuan Fang, Eike Tangermann, Richard D. Sandberg and Markus Klein
Fluids 2024, 9(8), 191; https://doi.org/10.3390/fluids9080191 - 21 Aug 2024
Viewed by 1309
Abstract
This paper introduces innovative approaches to enhance and develop one-equation RANS models using gene-expression programming. Two distinct strategies are explored: overcoming the limitations of the Boussinesq hypothesis and formulating a novel one-equation turbulence model that can accurately predict a wide range of turbulent [...] Read more.
This paper introduces innovative approaches to enhance and develop one-equation RANS models using gene-expression programming. Two distinct strategies are explored: overcoming the limitations of the Boussinesq hypothesis and formulating a novel one-equation turbulence model that can accurately predict a wide range of turbulent wall-bounded flows. A comparative analysis of these strategies highlights their potential for advancing RANS modeling capabilities. The study employs a single-case CFD-driven machine learning framework, demonstrating that machine-informed models significantly improve predictive accuracy, especially when baseline RANS predictions diverge from established benchmarks. Using existing training data, symbolic regression provides valuable insights into the underlying physics by eliminating ineffective strategies. This highlights the broader significance of machine learning beyond developing turbulence closures for specific cases. Full article
(This article belongs to the Section Turbulence)
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23 pages, 2942 KiB  
Article
Mechanism of the Oxidative Ring-Closure Reaction during Gliotoxin Biosynthesis by Cytochrome P450 GliF
by Muizz Qureshi, Thirakorn Mokkawes, Yuanxin Cao and Sam P. de Visser
Int. J. Mol. Sci. 2024, 25(16), 8567; https://doi.org/10.3390/ijms25168567 - 6 Aug 2024
Cited by 3 | Viewed by 1618
Abstract
During gliotoxin biosynthesis in fungi, the cytochrome P450 GliF enzyme catalyzes an unusual C–N ring-closure step while also an aromatic ring is hydroxylated in the same reaction cycle, which may have relevance to drug synthesis reactions in biotechnology. However, as the details of [...] Read more.
During gliotoxin biosynthesis in fungi, the cytochrome P450 GliF enzyme catalyzes an unusual C–N ring-closure step while also an aromatic ring is hydroxylated in the same reaction cycle, which may have relevance to drug synthesis reactions in biotechnology. However, as the details of the reaction mechanism are still controversial, no applications have been developed yet. To resolve the mechanism of gliotoxin biosynthesis and gain insight into the steps leading to ring-closure, we ran a combination of molecular dynamics and density functional theory calculations on the structure and reactivity of P450 GliF and tested a range of possible reaction mechanisms, pathways and models. The calculations show that, rather than hydrogen atom transfer from the substrate to Compound I, an initial proton transfer transition state is followed by a fast electron transfer en route to the radical intermediate, and hence a non-synchronous hydrogen atom abstraction takes place. The radical intermediate then reacts by OH rebound to the aromatic ring to form a biradical in the substrate that, through ring-closure between the radical centers, gives gliotoxin products. Interestingly, the structure and energetics of the reaction mechanisms appear little affected by the addition of polar groups to the model and hence we predict that the reaction can be catalyzed by other P450 isozymes that also bind the same substrate. Alternative pathways, such as a pathway starting with an electrophilic attack on the arene to form an epoxide, are high in energy and are ruled out. Full article
(This article belongs to the Special Issue Cytochrome P450 Mechanism and Reactivity)
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27 pages, 3487 KiB  
Article
Enhancing CFD Predictions with Explainable Machine Learning for Aerodynamic Characteristics of Idealized Ground Vehicles
by Charles Patrick Bounds, Shishir Desai and Mesbah Uddin
Vehicles 2024, 6(3), 1318-1344; https://doi.org/10.3390/vehicles6030063 - 31 Jul 2024
Cited by 3 | Viewed by 1939
Abstract
Computational fluid dynamic (CFD) models and workflows are often developed in an ad hoc manner, leading to a limited understanding of interaction effects and model behavior under various conditions. Machine learning (ML) and explainability tools can help CFD process development by providing a [...] Read more.
Computational fluid dynamic (CFD) models and workflows are often developed in an ad hoc manner, leading to a limited understanding of interaction effects and model behavior under various conditions. Machine learning (ML) and explainability tools can help CFD process development by providing a means to investigate the interactions in CFD models and pipelines. ML tools in CFD can facilitate the efficient development of new processes, the optimization of current models, and enhance the understanding of existing CFD methods. In this study, the turbulent closure coefficient tuning of the SST kω Reynolds-averaged Navier–Stokes (RANS) turbulence model was selected as a case study. The objective was to demonstrate the efficacy of ML and explainability tools in enhancing CFD applications, particularly focusing on external aerodynamic workflows. Two variants of the Ahmed body model, with 25-degree and 40-degree slant angles, were chosen due to their availability and relevance as standard geometries for aerodynamic process validation. Shapley values, a concept derived from game theory, were used to elucidate the impact of varying the values of the closure coefficients on CFD predictions, chosen for their robustness in providing clear and interpretable insights into model behavior. Various ML algorithms, along with the SHAP method, were employed to efficiently explain the relationships between the closure coefficients and the flow profiles sampled around the models. The results indicated that model coefficient β* had the greatest overall effect on the lift and drag predictions. The ML explainer model and the generated explanations were used to create optimized closure coefficients, achieving an optimal set that reduced the error in lift and drag predictions to less than 7% and 0.5% for the 25-degree and 40-degree models, respectively. Full article
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19 pages, 15778 KiB  
Article
Pressure Loss Modeling for Multi-Stage Obstacles in Pressurized Ducts
by Guillaume Bon, Ludovic Chatellier, Yves Le Guer, Cécile Bellot, Xavier Casiot and Laurent David
Energies 2024, 17(14), 3505; https://doi.org/10.3390/en17143505 - 17 Jul 2024
Cited by 1 | Viewed by 1207
Abstract
Estimating singular pressure losses for multi-stage obstacles in pressurized hydraulic ducts is a challenging task. An experimental study was conducted in a closed-loop hydrodynamic tunnel to characterize the pressure losses of a system consisting of a porous fibrous foam placed in front of [...] Read more.
Estimating singular pressure losses for multi-stage obstacles in pressurized hydraulic ducts is a challenging task. An experimental study was conducted in a closed-loop hydrodynamic tunnel to characterize the pressure losses of a system consisting of a porous fibrous foam placed in front of a bar rack. The pressure losses of different foam–rack configurations were measured over a range of inlet velocities in order to highlight the mutual influence of their characteristics on the flow. The interdependence between the two stages has been evidenced by both the experimental results and additional numerical simulations using RANS (Reynolds-Averaged Navier–Stokes Equations) simulations with a k-ω SST turbulent closure model. The pressure losses were first modeled using two approaches based on the assumption of either independence or full dependence between the stages. The respective advantages and limitations of these approaches led to an improved analytical formula that considers the transition of the flow from the porous foam to the bar rack. By taking into account an empirical transition factor, the proposed model improves the head loss prediction for all tested configurations, with an average relative error between the formula and experimental results less than that of the two simpler approaches. This study improves our understanding of global pressure losses in multi-stage systems that include a porous foam or other filtering or clogging media in front of bar racks. Full article
(This article belongs to the Section B: Energy and Environment)
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15 pages, 4639 KiB  
Article
Experimental Study of Pollutant Emissions from Biomass Combustion and Modeling of PM Transportation
by François Delcourt, Abdelkader Izerroukyene, Damien Méresse, David Uystepruyst, François Beaubert and Céline Morin
Energies 2024, 17(11), 2586; https://doi.org/10.3390/en17112586 - 27 May 2024
Cited by 3 | Viewed by 1304
Abstract
Experimental measurements and modeling have been performed in the chimney of a biomass boiler to study the gaseous and particulate matter (PM) emissions during the combustion of wood pellets. A 10 kW boiler with an underfeed burner is equipped with different sensors located [...] Read more.
Experimental measurements and modeling have been performed in the chimney of a biomass boiler to study the gaseous and particulate matter (PM) emissions during the combustion of wood pellets. A 10 kW boiler with an underfeed burner is equipped with different sensors located in the chimney (anemometer, thermocouples). The PM emissions were measured in the chimney through the engine exhaust particle sizer (EEPS) technique. Moreover, the gaseous emissions (CO2, CO, total hydrocarbons THC, O2) were obtained through infrared (IR) spectroscopy and flame ionization detector (FID). The emissions were recorded during the steady phase of the boiler and averaged over several tests. Four locations were investigated in the chimney to evaluate the evolution of the particle size and the potential deposition on the surface. The experimental results were compared with a CFD model with particle transportation. The modeling of turbulent flow in the chimney is based on a Reynolds-averaged Navier–Stokes (RANS) approach with turbulent viscosity closure. To account for flow anisotropy, the v2¯f turbulence model was selected for this study. The effect of turbulent fluctuations on the discrete phase is considered by the discrete random walk (DRW) turbulent dispersion model. The results obtained provide access to the topology of the carrier phase flow as well as the complete distribution of the particle field within the chimney enclosure. Advanced measurement of pollutant emissions and modeling of the PM transportation are developed for the first time in a domestic biomass boiler operating in real conditions. Experimental results demonstrate several relevant information. The CO and THC emissions show a similar evolution versus time. The PM granulometric distribution measured along the chimney highlights the particle agglomeration phenomena. Moreover, the CFD model and experimental results give similar results in terms of flow characteristics and PM granulometry. Full article
(This article belongs to the Collection Feature Papers in Energy, Environment and Well-Being)
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21 pages, 23944 KiB  
Article
Direct Numerical Simulation Analysis of the Closure of Turbulent Scalar Flux during Flame–Wall Interaction of Premixed Flames within Turbulent Boundary Layers
by Umair Ahmed, Sanjeev Kumar Ghai and Nilanjan Chakraborty
Energies 2024, 17(8), 1930; https://doi.org/10.3390/en17081930 - 18 Apr 2024
Viewed by 1166
Abstract
The statistical behaviour and modelling of turbulent fluxes of the reaction progress variable and non-dimensional temperature in the context of Reynolds-Averaged Navier–Stokes (RANS) simulations have been analysed for flame–wall interactions within turbulent boundary layers. Three-dimensional Direct Numerical Simulation (DNS) databases of two different [...] Read more.
The statistical behaviour and modelling of turbulent fluxes of the reaction progress variable and non-dimensional temperature in the context of Reynolds-Averaged Navier–Stokes (RANS) simulations have been analysed for flame–wall interactions within turbulent boundary layers. Three-dimensional Direct Numerical Simulation (DNS) databases of two different flame–wall interaction configurations—(i) statistically stationary oblique wall quenching (OWQ) of a V-flame in a turbulent channel flow and (ii) unsteady head-on quenching (HOQ) of a statistically planar flame propagating across a turbulent boundary layer—have been considered for this analysis. Scalar fluxes of both the temperature and reaction progress variable exhibit counter-gradient behaviour at all times during unsteady HOQ of statistically planar turbulent premixed flames considered here. In the case of statistically stationary V-flame OWQ, the scalar fluxes of both reaction progress variable and temperature exhibit counter-gradient behaviour before quenching, but gradient behaviour has been observed close to the wall once the flame begins to quench. The weakening of the effects of thermal expansion close to the wall as a result of flame quenching gives rise to a gradient type of transport for the streamwise component in the oblique quenching of the V-flame. It has been found that the relative orientation of the flame normal vector with respect to the wall normal vector needs to be accounted for in the algebraic scalar flux closure, which can be applied to different flame/flow configurations. An existing algebraic scalar flux model has been modified in this analysis for flame–wall interaction within turbulent boundary layers, and it has been demonstrated to capture the turbulent fluxes of the reaction progress variable and non-dimensional temperature reasonably accurately for both configurations considered here based on a priori DNS analysis. Full article
(This article belongs to the Special Issue Experiments and Simulations of Combustion Process II)
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21 pages, 3275 KiB  
Article
Deep Reinforcement Learning-Augmented Spalart–Allmaras Turbulence Model: Application to a Turbulent Round Jet Flow
by Lukas M. Fuchs, Jakob G. R. von Saldern, Thomas L. Kaiser and Kilian Oberleithner
Fluids 2024, 9(4), 88; https://doi.org/10.3390/fluids9040088 - 9 Apr 2024
Cited by 1 | Viewed by 2048
Abstract
The purpose of this work is to explore the potential of deep reinforcement learning (DRL) as a black-box optimizer for turbulence model identification. For this, we consider a Reynolds-averaged Navier–Stokes (RANS) closure model of a round turbulent jet flow at a Reynolds number [...] Read more.
The purpose of this work is to explore the potential of deep reinforcement learning (DRL) as a black-box optimizer for turbulence model identification. For this, we consider a Reynolds-averaged Navier–Stokes (RANS) closure model of a round turbulent jet flow at a Reynolds number of 10,000. For this purpose, we augment the widely utilized Spalart–Allmaras turbulence model by introducing a source term that is identified by DRL. The algorithm is trained to maximize the alignment of the augmented RANS model velocity fields and time-averaged large eddy simulation (LES) reference data. It is shown that the alignment between the reference data and the results of the RANS simulation is improved by 48% using the Spalart–Allmaras model augmented with DRL compared to the standard model. The velocity field, jet spreading rate, and axial velocity decay exhibit substantially improved agreement with both the LES reference and literature data. In addition, we applied the trained model to a jet flow with a Reynolds number of 15,000, which improved the mean field alignment by 35%, demonstrating that the framework is applicable to unseen data of the same configuration at a higher Reynolds number. Overall, this work demonstrates that DRL is a promising method for RANS closure model identification. Hurdles and challenges associated with the presented methodology, such as high numerical cost, numerical stability, and sensitivity of hyperparameters are discussed in the study. Full article
(This article belongs to the Section Mathematical and Computational Fluid Mechanics)
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20 pages, 4224 KiB  
Article
Performance of Reynolds Averaged Navier–Stokes and Large Eddy Simulation Models in Simulating Flows in a Crossflow Ultraviolet Reactor: An Experimental Evaluation
by Shuai Zhang and Adrian Wing-Keung Law
Water 2024, 16(2), 271; https://doi.org/10.3390/w16020271 - 12 Jan 2024
Cited by 3 | Viewed by 1404
Abstract
Computational Fluid Dynamics (CFD) has been increasingly adopted as a design tool for the simulation of UV disinfection efficiency and the optimization of the configuration of a UV reactor. However, the performance of CFD with different turbulence closures may vary significantly. In the [...] Read more.
Computational Fluid Dynamics (CFD) has been increasingly adopted as a design tool for the simulation of UV disinfection efficiency and the optimization of the configuration of a UV reactor. However, the performance of CFD with different turbulence closures may vary significantly. In the present study, an experimental evaluation was performed to assess the performance of CFD with five Reynolds Averaged Navier–Stokes (RANS) turbulence closures and three Large Eddy Simulation (LES) sub-grid scale (SGS) models. A simplified crossflow reactor with a single lamp sleeve was fabricated for the experimental measurements and numerical simulations. Overall, the superior performance of LES compared to RANS models in flow predictions within a complex configuration is demonstrated. Full article
(This article belongs to the Special Issue CFD in Fluid Machinery Design and Optimization)
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9 pages, 254 KiB  
Article
A Mathematically Exact and Well-Determined System of Equations to Close Reynolds-Averaged Navier–Stokes Equations
by Sungmin Ryu
Mathematics 2023, 11(24), 4926; https://doi.org/10.3390/math11244926 - 11 Dec 2023
Viewed by 2113
Abstract
Since Sir Osborne Reynolds presented the Reynolds-averaged Navier–Stokes (RANS) equations in 1895, the construction of complete closure for RANS equations has been regarded as extremely challenging. Taking into account that the Navier–Stokes equations are not coherent for instantaneous and mean flows, a body [...] Read more.
Since Sir Osborne Reynolds presented the Reynolds-averaged Navier–Stokes (RANS) equations in 1895, the construction of complete closure for RANS equations has been regarded as extremely challenging. Taking into account that the Navier–Stokes equations are not coherent for instantaneous and mean flows, a body of knowledge outside the scope of classical mechanics may be amenable to the closure problem. In this regard, the methodology of physics-to-geometry transformation, which is coherent for both flows, is applied to RANS equations to construct six additional equations. The proposed equations stand out from existing RANS closure models and turbulence quantity transport equations in two respects: they are mathematically exact and well-determined. Full article
31 pages, 6312 KiB  
Article
Modified Accuracy of RANS Modeling of Urban Pollutant Flow within Generic Building Clusters Using a High-Quality Full-Scale Dispersion Dataset
by Mohammad Reza Kavian Nezhad, Khashayar RahnamayBahambary, Carlos F. Lange and Brian A. Fleck
Sustainability 2023, 15(19), 14317; https://doi.org/10.3390/su151914317 - 28 Sep 2023
Cited by 2 | Viewed by 2325
Abstract
To improve the reliability of the computational fluid dynamics (CFD) models of wind-driven pollutant dispersion within urban settings, a re-calibration study is conducted to optimize the standard kε model. A modified optimization framework based on the genetic algorithm is adapted to [...] Read more.
To improve the reliability of the computational fluid dynamics (CFD) models of wind-driven pollutant dispersion within urban settings, a re-calibration study is conducted to optimize the standard kε model. A modified optimization framework based on the genetic algorithm is adapted to alleviate the computational expenses and to further identify ranges for each empirical coefficient to achieve the most reliable and accurate predictions. A robust objective function is defined, incorporating both the flow parameters and pollutant concentration through several linear and logarithmic measures. The coefficients are trained using high-quality and full-scale tracer experiments in a mock urban arrangement simulating a building array. The proposed ranges are 0.14Cμ0.15, 1.30Cε11.46, 1.68Cε21.80, 1.12σε1.20, and 0.87σk1.00. A thorough evaluation of the predicted flow and concentration fields indicates the modified closure is effective. The fraction of predictions within the acceptable ranges from measurements has increased by 8% for pollutant concentration and 27% for turbulence kinetic energy. The generality of the calibrated model is further tested by modeling additional cases with different meteorological conditions, in which the calculated validation metrics attest to the noteworthy improvements in predictions. Full article
(This article belongs to the Special Issue Computational Fluid Dynamics Simulation: Application in Industries)
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16 pages, 7230 KiB  
Article
An Investigation of Wave-Driven Current Characteristics across Fringing Reefs under Monochromatic Waves
by Tao Yuan, Yu Yao, Zhuangzhi Li and Conghao Xu
J. Mar. Sci. Eng. 2023, 11(10), 1843; https://doi.org/10.3390/jmse11101843 - 22 Sep 2023
Cited by 1 | Viewed by 1600
Abstract
The aim of this study is to better understand cross-reef wave-driven current characteristics, which are crucial to biological, ecological, and geomorphological processes within coral reefs. This study reports a set of new wave flume measurements to assess flow along the water depth and [...] Read more.
The aim of this study is to better understand cross-reef wave-driven current characteristics, which are crucial to biological, ecological, and geomorphological processes within coral reefs. This study reports a set of new wave flume measurements to assess flow along the water depth and across a fringing reef profile under the action of a plunging breaker. Laboratory results are presented in view of cross-reef variations in both the wave height and the mean water level (MWL); the vertical profiles of wave-averaged mean currents below the wave trough and along the reef are also presented. To resolve the two-dimensional vertical (2DV) flow characteristics across the reef, Reynolds-Averaged Navier–Stokes (RANS) equations were solved using k-ω SST closure, modified to improve stability, and a Volume of Fluid (VOF) approach was used to capture the water surface. This numerical model was first validated via experimental measurements in view of waves and flows. It was then used to analyze the cross-reef distributions of the mean flow field, turbulent kinetic energy (TKE), and Reynolds shear stress across the reef. Full article
(This article belongs to the Section Coastal Engineering)
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